c.weight

arith

Fuzzy and crisp fixed point cluster analysis 
  based on Mahalanobis distance and linear regression fixed point
  clusters. Semi-explorative, semi-model-based clustering methods, 
  operating on n*p data, do not need prespecification of number of 
  clusters, produce overlapping clusters. Symmetric and asymmetric
  discriminant projections separate
  groups optimally, used to visualize the separation of
  groupings, visual cluster validation. Clusterwise linear 
  regression by normal mixture modeling. Cluster validation statistics
  for distance based clustering. Clusterwise cluster stability assessment. 
  DBSCAN clustering. Interface functions for many clustering methods 
  implemented in R.

c.weight: Weight function for AWC

Description

Usage

Arguments

Value

See Also

Examples